{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import orjsonl\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       model  ai_corrupt\n",
      "0                      short    0.693599\n",
      "1                      short    0.295584\n",
      "2                      short    0.705920\n",
      "3                      short    0.802875\n",
      "4                      short    0.007526\n",
      "...                      ...         ...\n",
      "79995  TIPO + truncated long    0.162923\n",
      "79996  TIPO + truncated long    0.204599\n",
      "79997  TIPO + truncated long    0.859470\n",
      "79998  TIPO + truncated long    0.559704\n",
      "79999  TIPO + truncated long    0.603184\n",
      "\n",
      "[80000 rows x 2 columns]\n",
      "0.00010383129119873047 0.999702689354308\n"
     ]
    }
   ],
   "source": [
    "all_values = []\n",
    "datas = []\n",
    "names = [\"short\", \"truncated long\", \"TIPO + short\", \"TIPO + truncated long\"]\n",
    "model_order = [\n",
    "    \"short\",\n",
    "    \"TIPO + short\",\n",
    "    \"truncated long\",\n",
    "    \"TIPO + truncated long\",\n",
    "]\n",
    "for name, data in zip(names, orjsonl.load(\"./coyo-ai-corrupt.jsonl\", \"rb\")):\n",
    "    datas.extend((name, d) for d in data)\n",
    "    all_values.extend(data)\n",
    "for name, data in zip(names, orjsonl.load(\"./gbc-ai-corrupt.jsonl\", \"rb\")):\n",
    "    datas.extend((name, d) for d in data)\n",
    "    all_values.extend(data)\n",
    "all_values = np.array(all_values)\n",
    "df = pd.DataFrame(datas, columns=[\"model\", \"ai_corrupt\"])\n",
    "print(df)\n",
    "print(df[\"ai_corrupt\"].min(), df[\"ai_corrupt\"].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00010383129119873047 0.999702689354308 0.33843261400867625 0.548080367099696\n"
     ]
    }
   ],
   "source": [
    "min = df[\"ai_corrupt\"].min()\n",
    "max = df[\"ai_corrupt\"].max()\n",
    "std = df[\"ai_corrupt\"].std()\n",
    "mean = df[\"ai_corrupt\"].mean()\n",
    "\n",
    "# df[\"ai_corrupt\"] = (df[\"ai_corrupt\"] - mean) / std\n",
    "print(min, max, std, mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scale = 1.0\n",
    "box_scale = 0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\apoll\\AppData\\Local\\Temp\\ipykernel_16532\\3437339824.py:64: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  quantiles = df_sorted.groupby(\"model\")[\"ai_corrupt\"].quantile(\n",
      "C:\\Users\\apoll\\AppData\\Local\\Temp\\ipykernel_16532\\3437339824.py:74: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  means = df_sorted.groupby(\"model\")[\"ai_corrupt\"].mean()\n",
      "C:\\Users\\apoll\\AppData\\Local\\Temp\\ipykernel_16532\\3437339824.py:75: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  std = df_sorted.groupby(\"model\")[\"ai_corrupt\"].std()\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 3900x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Quantiles for each model:\n",
      "model                      \n",
      "TIPO + truncated long  0.05    0.017085\n",
      "                       0.25    0.231321\n",
      "                       0.50    0.617368\n",
      "                       0.75    0.878598\n",
      "                       0.95    0.980683\n",
      "truncated long         0.05    0.012247\n",
      "                       0.25    0.181891\n",
      "                       0.50    0.553895\n",
      "                       0.75    0.856910\n",
      "                       0.95    0.976258\n",
      "TIPO + short           0.05    0.017356\n",
      "                       0.25    0.252004\n",
      "                       0.50    0.628602\n",
      "                       0.75    0.881953\n",
      "                       0.95    0.979901\n",
      "short                  0.05    0.016525\n",
      "                       0.25    0.229442\n",
      "                       0.50    0.609118\n",
      "                       0.75    0.873913\n",
      "                       0.95    0.979215\n",
      "Name: ai_corrupt, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate statistics for sorting\n",
    "df_stats = (\n",
    "    df.groupby(\"model\").agg({\"ai_corrupt\": [\"mean\", lambda x: x.mean()]}).reset_index()\n",
    ")\n",
    "df_stats.columns = [\"model\", \"mean\", \"ai_corrupt\"]\n",
    "\n",
    "# Choose sorting method (uncomment the desired method)\n",
    "# df_stats_sorted = df_stats.sort_values('median', ascending=False)\n",
    "df_stats_sorted = df_stats.sort_values(\"mean\", ascending=False)\n",
    "\n",
    "# Sort the original dataframe based on the stats\n",
    "df_sorted = df.set_index(\"model\").loc[model_order].reset_index()\n",
    "# print(df_stats)\n",
    "\n",
    "# Create a figure and axes\n",
    "fig, ax = plt.subplots(figsize=(13, 5), dpi=300)\n",
    "\n",
    "df_sorted[\"model\"] = pd.Categorical(\n",
    "    df_sorted[\"model\"],\n",
    "    categories=reversed(list(df_sorted[\"model\"].unique())),\n",
    "    ordered=True,\n",
    ")\n",
    "positions = [i * y_scale for i in range(len(model_order))]\n",
    "# Create the horizontal boxplot using the sorted data\n",
    "boxplot = df_sorted.boxplot(\n",
    "    column=\"ai_corrupt\",\n",
    "    by=\"model\",\n",
    "    vert=False,\n",
    "    ax=ax,\n",
    "    patch_artist=True,\n",
    "    medianprops={\"color\": \"red\"},\n",
    "    showfliers=False,\n",
    "    color={\n",
    "        \"boxes\": \"#54A5F0\",\n",
    "        \"whiskers\": \"#4016F5\",\n",
    "        \"medians\": \"red\",\n",
    "        \"caps\": \"black\",\n",
    "    },\n",
    "    positions=positions,\n",
    "    widths=[box_scale] * len(model_order),\n",
    ")\n",
    "\n",
    "# Customize the plot\n",
    "ax.set_title(\"AI Corrupt Score Boxplot by Prompt\")\n",
    "ax.set_ylabel(\"Prompt\")\n",
    "ax.set_xlabel(\"AI Corrupt Score (Higher is Better) →\")\n",
    "\n",
    "# Remove the automatically generated suptitle\n",
    "plt.suptitle(\"\")\n",
    "\n",
    "# Adjust the y-axis tick labels\n",
    "plt.yticks(rotation=0)\n",
    "\n",
    "# Adjust the data range and tick marks\n",
    "min_score = df[\"ai_corrupt\"].min()\n",
    "max_score = df[\"ai_corrupt\"].max()\n",
    "range_margin = (max_score - min_score) * 0.05\n",
    "# ax.set_xlim(-1.5, 1.5)\n",
    "ax.xaxis.set_major_locator(plt.MultipleLocator(0.25))\n",
    "\n",
    "# Calculate and add quantile information\n",
    "quantiles = df_sorted.groupby(\"model\")[\"ai_corrupt\"].quantile(\n",
    "    [0.05, 0.25, 0.5, 0.75, 0.95]\n",
    ")\n",
    "reverse_map = {\n",
    "    v1: v2\n",
    "    for v1, v2 in zip(\n",
    "        df_sorted[\"model\"].unique(), reversed(df_sorted[\"model\"].unique())\n",
    "    )\n",
    "}\n",
    "\n",
    "means = df_sorted.groupby(\"model\")[\"ai_corrupt\"].mean()\n",
    "std = df_sorted.groupby(\"model\")[\"ai_corrupt\"].std()\n",
    "\n",
    "# print(quantiles)\n",
    "for i, model in enumerate(df_sorted[\"model\"].unique()):\n",
    "    model = reverse_map[model]\n",
    "    quantile_text = (\n",
    "        f\"mean: {means.loc[model]:.4f}\\n\"\n",
    "        f\"std : {std.loc[model]:.4f}\\n\"\n",
    "        f\"95% : {quantiles.loc[model, 0.95]:.4f}\\n\"\n",
    "        f\"75% : {quantiles.loc[model, 0.75]:.4f}\\n\"\n",
    "        f\"50% : {quantiles.loc[model, 0.50]:.4f}\\n\"\n",
    "        f\"25% : {quantiles.loc[model, 0.25]:.4f}\\n\"\n",
    "        f\"05% : {quantiles.loc[model, 0.05]:.4f}\"\n",
    "    )\n",
    "    ax.annotate(\n",
    "        quantile_text,\n",
    "        xy=(1.02, i * y_scale),\n",
    "        xycoords=(\"axes fraction\", \"data\"),\n",
    "        va=\"center\",\n",
    "        ha=\"left\",\n",
    "        fontsize=7,\n",
    "    )\n",
    "\n",
    "# Display the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Log quantile information\n",
    "print(\"\\nQuantiles for each model:\")\n",
    "print(quantiles)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
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 },
 "nbformat": 4,
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